Auto-encoder Video Anomaly Detection Algorithm Based on Improved VGG16
When using the auto-encoder structure neural network to process video anomaly detection tasks,the U-Net style auto-encoder cannot fully extract more useful feature information when facing complex data sets due to the shallow depth of the encoder layer.At the same time,when training the model,MSE is used,only considering the pixel level similarity between the predicted frame and the real frame.For complex scenes,pixel level similarity may not accurately determine the similarity between the predicted frame and the real frame.To solve the above problems,the U-Net style auto-encoder is improved,and a video anomaly detection algorithm using the improved VGG16 as the encoder is proposed.At the same time,the structure similarity(SSIM)loss function is added on the basis of MSE.The improved VGG16 removes the fully connected layer and adds residual connections to prevent feature degradation.SSIM is added to optimize the network by calculating pixel level similarity while also calculating image brightness,contrast,and structural similarity.The experimental results show that the improved algorithm achieves a detection performance of 95.91％on the Ped2 dataset and 84.89％on the Avenue dataset,which is 0.80％and 0.19％higher than that of the previous method,respectively,verifying the ef-fectiveness of the proposed method.
万方数据
维普
